Análisis y Predicción de Series Temporales

Lab 10

• RNN_extra_slides.pdf

Recomendamos ver el siguiente vídeo para la próxima sesión:
Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) by Brandon Rohrer

• Lab10_XGBoostForecasting.html

• Lab10_MLPforecasting_grupoB.html
  R Source Code

• Lab10_FT_DemandTemperature_solution_grupoB.html
  R Source Code

Lab 8

• Lab8_DynamicRegressionModelsForecastingExample.html
• Lab8_DynamicRegressionModelsForecastingExample.R
• macro_econ_TR.csv.
• macro_econ_TS.csv

Lab 7

• Lab7_DynamicRegressionModels_GrupoB.html
  R Source Code
  
• Lab7_TF_Seasonal_GrupoB.R

Lab 6

• Lab6_Prophet_v7.html

Lab 4

• Lab. 4 SARIMA models R Source Code

• Lab. 4 ARIMA models
  R Source Code

• Suggested Reading:

  • Ch.09 of Hyndman and Athanasopoulos (2021), Sections 9.1 to 9.5. For the second edition see Chapter 8 of FPP2, Sections 8.1 to 8.8
  • Ch.11 of Krispin (2019)
  • Ch02 of Shumway and Stoffer (2017)

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Lab 2

• Lab. 2 ARMA models
  R Source Code

• Suggested Reading:

  • Ch.09 of FPP3, Sections 9.1 to 9.5
  • Ch.11 of Krispin (2019)
  • For more mathematical background up to Ch02 of Shumway and Stoffer (2017)

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Lab 1

• Lab. 1 Decomposition
• Lab. 1. Additional code, Stochastic Processes
  R Source Code

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References

Hyndman, R. J., and G. Athanasopoulos. 2021. Forecasting: Principles and Practice. OTexts. https://otexts.com/fpp3/.

Krispin, Rami. 2019. Hands-on Time Series Analysis with r: Perform Time Series Analysis and Forecasting Using r. Packt Publishing Ltd. https://github.com/PacktPublishing/Hands-On-Time-Series-Analysis-with-R.

Shumway, R. H., and D. S. Stoffer. 2017. Time Series Analysis and Its Applications: With r Examples. Springer Texts in Statistics. Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-52452-8.